This statistic displays the value of commercial fishing and aquaculture production in the Great Barrier Reef (GBR) in Australia in fiscal year 2015/2016, by region. In this financial year, the production value of commercial fishing and aquaculture in the Wet Tropics amounted to around ** million Australian dollars.

This statistic displays the results of a survey about the leading threats to the Great Barrier Reef (GBR) according to residents in Australia as at June 2017. During the survey period, around 44 percent of respondents in Australia stated that they believed the biggest threat to the health of the Great Barrier Reef was climate change.

Summary statistics for variation in the distribution and functional assemblage of reef fishes on submerged and emergent reefs in the central section of the Great Barrier Reef.

This statistic displays the value of commercial fishing and aquaculture production in the Great Barrier Reef (GBR) region in Australia in the fiscal year 2016, by type. In this financial year, the production value of commercial fishing using line, net, pot and trawl in the Great Barrier Reef amounted to around 95 million Australian dollars.

Financial overview and grant giving statistics of Great Barrier Reef Foundation USA Inc.

This statistic displays the results of a survey about the public opinion regarding the Great Barrier Reef (GBR) in Australia as at June 2017. During the survey period, 95 percent of Australian and international respondents stated that they agreed that the Great Barrier Reef was an iconic Australian landmark that contributed to Australia's national identity and international standing.

Spreadsheet of Shark Control Program shark catch numbers in the Great Barrier Reef Marine Park by species, date, area, location, fate and number

Spreadsheet of Shark Control Program shark catch numbers in the Great Barrier Reef Marine Park by species, date, area, location, fate and number

This statistic displays the value of recreational expenditure in the Great Barrier Reef (GBR) region in Australia in the fiscal year 2016, by type. In this financial year, the recreational expenditure in the Great Barrier Reef on equipment amounted to around *** million Australian dollars.

Table columns include analyte, Pre-2015 S1, S2, S3 and Post-2015, study area, parameter, estimate of the parameter, standard error of the estimate, t-value, p-value, the transformed FDR p-value, logical for parameter significance at 5% FDR and parameter group of intercept, site, project, trend and seasonal terms. (XLS)

The GBR (Guided Bone Regeneration) and GTR (Guided Tissue Regeneration) barrier membranes market plays a crucial role in modern dental and orthopedic surgeries. These specialized membranes are designed to facilitate the regeneration of bone and tissue by acting as barriers that prevent the infiltration of unwanted c

These data accompany a manuscript that examines data from the Great Barrier Reef MMP inshore water quality program (MMP WQ) and assesses the impact that the sampling re-design had on the power to detect trends in six priority water quality analytes, which is a primary objective of the MMP WQ.

The manuscript details are

Luke R. Lloyd-Jones, Petra M. Kuhnert, Emma Lawrence, Stephen E. Lewis, Jane Waterhouse, Renee K. Gruber and Frederieke J. Kroon. Sampling re-design increases power to detect change in the Great Barrier Reef’s inshore water quality. 2022.

The dataset in file GBR-MMPDataset-processed-power.csv file contains data from the MMP WQ monitoring program.

The current MMP WQ monitors the inshore waters of the GBR Marine Park across five of the six Natural Resource Management (NRM) regions: Cape York, Wet Tropics, Burdekin, Mackay-Whitsunday, and Fitzroy. In our study, we consider monitoring data collected across four study areas within three of these NRM regions, namely the Russell-Mulgrave and Tully study areas (within the Wet Tropics NRM region), the Burdekin study area (i.e., the Burdekin NRM region), and the Mackay-Whitsunday study area (i.e., the Mackay-Whitsunday NRM region).

The data uses inshore water quality data obtained from ambient monitoring using grab samples at fixed locations across these four study areas from 2005 to 2019. The data used are combined from across two research organisations that monitor different spatiotemporal components of the GBR. The institutions include the Australian Institute of Marine Science (AIMS) and James Cook University (JCU).

The MMP WQ conducts ambient water quality monitoring, including grab sampling during non-event periods (i.e., outside river flooding events), to collect a suite of physical, chemical, and biological water quality analytes at each sampling location.

These data are for six water quality analytes, namely total suspended solids (TSS), Secchi disc depth (Secchi), Chlorophyll a (Chl-a), particulate nitrogen (PN), particulate phosphorus (PP), and nitrate/nitrite (NOx). These six analytes are considered relatively robust indicators that integrate several bio-physical processes in the coastal ocean, and water quality guideline values are available for all six of these analytes. Lineage: The data presented in GBR-MMPDataset-processed-power.csv have been preprocessed from the raw data. The raw data were received from AIMS and processed for the power analysis as below.

For each sampling time point at each sampling location, values for each water quality analyte were initially averaged over any duplicate measurements, and subsequently depth-averaged by taking the mean of surface and bottom values, which is the standard for the MMP WQ reporting. Measurements of nitrite and nitrate in the tropical coastal ocean are often below the detection limit (BDL) of analytical instruments, which are reported as half the detection limit (1/2DL) in the MMP WQ dataset. NOx measurements can therefore represent (1) the sum of two BDL measurements, or (2) comprise a BDL measurement and a concentration measurement above the detection limit. For NOx, we investigated the implications of imputing BDL values with 1/2DL values on statistical power by comparing results with two other methods (see manuscript for more details).

Study areas, survey locations and seasonal sampling frequency for the subset of the marine monitoring program used in the power analysis detailed by design i.e., 2005–2014 or 2015–2019.

Table columns include REGION—four study areas, DELTA—fractional year-on-year change, ANALYTE—analyte analysed, MEAN_TIME_Q10—time in years for projected linear trend to go below the 10th percentile of the post-2015 data, MEAN_TIME_Q90—time in years for projected linear trend to go above the 90th percentile of the post-2015 data. (XLS)

The dashboard set comprises individual web pages for each sensor/location. Each dashboard includes a map showing the location of the station, basic statistics and time series plots. If enough data is available (more than 10 years), a climatology of the temperature record is calculated. The SSTAARS climatology is also plotted along the sensor data. Hourly time series plots are also available at each instrument’s depth.

See "Interactive map of this dataset" resource link below for a navigation map to the dashboard web pages.

This comprehensive quality-controlled data set is to assist the delivery of the data to better characterise thermal stress events on the GBR to users.

The primary data set is temperature from over 100 permanent temperature logger locations within the reef from the AIMS temperature logger program and other platforms, which include both mobile gliders and drifting buoys to permanent weather stations and moorings. Summary plots of the data can be interrogated and daily climatologies are provided so users can quickly determine the thermal history at each site. Other relevant data sets are provided from multiple observing platforms with a summary plot. Some data sets have well developed websites and so a link to those sites and data sources are also provided for these.

Methods:

For each sensor, in reef temperature loggers, mooring instruments, AIMS weather stations and QLD wave buoys, the data is extracted for 2015, 2016 and 2017. The records are aggregated into hourly intervals and the climatology extracted from the full record (when more than 10 years of data exist), as the mean of daily average for each day of the year. Basic statistics for 2016 and 2017 are calculated and the heat accumulation indicators (NOAA’s degree-heating week, and the maximum monthly mean) extracted for the site and the year. In a second tab, the time series of each location is plotted. Many of the elements of the dashboard are dynamic, so the user can zoom in/out or print sections of the plots. The dashboard is generated using a dedicated R code.

The main code used to generate the dashboards is available on GitHub: https://github.com/eatlas/GBR_NESP-TWQ-4.2_AIMS_Water-temperature-dashboards

Limitations of the data:

Many of the sensors contain data outside the project time frame (2015-2017). However, this data was only used to calculate the climatology, when more than 10 years of data exist. Data outside the project time frame are not plotted in the dashboards. Data is available at AIMS DATA centre.

Format:

The dashboards are individual HTML files. The original data can be downloaded from AIMS data centre (temperature loggers, moorings and weather stations, https://www.aims.gov.au/docs/data/data.html ) or from Queensland Environment (wave buoys, https://www.qld.gov.au/environment/coasts-waterways/beach/monitoring/waves-sites )

References:

Drivers of Bleaching on the Great Barrier Reef - Compilation of temperature data from 2015, 2016 and 2017, https://eatlas.org.au/gbr/nesp-twq-4-2-temperature-data-2015-17

Files Location:

The code for this project is available on GitHub: https://github.com/eatlas/GBR_NESP-TWQ-4.2_AIMS_Water-temperature-dashboards

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2018-2021-NESP-TWQ-4\4.2_Oceanographic-drivers-of-bleaching\data\2020-08-05_GBR_AIMS_NESP-TWQ-4-2_Temp-dashboard_2015-17

The Oral Guided Bone Regeneration (GBR) and Guided Tissue Regeneration (GTR) Membrane market has emerged as a vital segment within the dental and orthopedic industries, primarily aimed at addressing significant challenges related to bone and tissue repair. GBR and GTR membranes play a crucial role in enhancing the n

This dataset shows various statistics of photic depth across the Great Barrier Reef (GBR). Data are broken into 35 zones along and across the GBR and photic depth is derived from 11 years of MODIS Aqua data. The data included is: 1. The statistical strength of correlation between standardized photic depth and freshwater discharges the GBR. 2. The mean photic depth and the main physical environmental variables that need to be controlled for when assessing how volumes of river freshwater discharges influence photic depth. 3. Statistics of photic depth controlled to remove the effects of main physical environmental variables (wave height, tidal range) used when assessing how volumes of river freshwater discharges. Data are into dry years (2002 to 2006) or wet years (2007 ¿ 2012).

Water clarity is a key parameter affecting the health of coastal marine systems and their tourism values. We investigated the relationship between volumes of river freshwater discharges of major rivers (from DERM) and the water clarity in 35 zones along and across the GBR waters within the Fitzroy, Whitsundays, Burdekin, Southern and Northern Wet Tropics. For Cape York, water clarity was related to rainfall as a proxy, since river data were incomplete. We used daily 11-years (2002-2013) MODIS-Aqua remote sensing data at 1 km2 resolution, to investigate time scales and processes affecting water clarity in these regions. In all coastal, inshore and lagoonal regions except for Cape York, photic depth was strongly negatively related to the freshwater discharge of the main rivers. The declines started with the onset of river floods, and water clarity typically took 150¿ 260 days until complete recovery. The relationship between photic depth and rivers was strongest in the Northern Wet Tropics, the initiation area of outbreaks of crown-of-thorns starfish, where effects were strong even on the outer shelf. Previous conclusions that river runoff predominantly affects the inshore of the GBR have therefore to be revised for the Central and Northern GBR. The results were used in the setting of regional ecologically relevant targets for fine sediment in the Burnett-Mary and Wet Tropics WQIPs, and will likely be used for other WQIPs.

The analyses are based on three sets of data: 1) Daily Modis Aqua satellite data from 2002 - 2013, processed as described previously (Weeks et al. 2012, Logan et al. 2013, Fabricius et al. 2014). 2) Daily data of freshwater discharge volumes of the main rivers for the same time period, provided by the State of Queensland, Department of Environment and Heritage Protection (DEHP). 3) For the Normanby River, the discharge station only came online late 2005. Therefore, most of the first four years of daily discharge data for the main river in this region are missing (Stewart and Endeavour Rivers are much smaller than the large Normanby). Also missing are any form of river discharge information for the whole northern half of the region. As an alternative to river discharge data, we used daily rainfall data from the Lockhart River rainfall gauge for Cape York, which is located relatively centrally in this ~400 km long band. Daily rainfall data were obtained from the Australian Bureau of Meteorology (http://www.bom.gov.au/oceanography/projects/abslmp/data/index.shtml).

Method:

We spatially aggregated the data into 15 zones for the Fitzroy and Whitsundays region, and 5 zones each for the Burdekin, Southern and Northern Wet Tropics, and Cape York regions. For the Whitsundays, Burdekin, Wet Tropics and Cape York, five bands were defined parallel to the coastline: - Coastal: 0 ¿ 0.1 fractional units across the GBR - Inshore: 0.1 ¿ 0.25 fractional units across the GBR - Lagoon: 0.25 ¿ 0.45 fractional units across the GBR - Midshelf: 0.45 ¿ 0.65 fractional units across the GBR - Outer shelf: 0.65 ¿ 1 fractional units across the GBR

The Fitzroy region cannot be partitioned up into simple coast-parallel bands, due to its geomorphology around to the Capricorn-Bunkers and Swains complex, and the estuarine Keppel Bay. Consequently, the Fitzroy region was partitioned according to a combination of geomorphological regions and boundary rules (based on distances from coastlines and bioregions) so to reflect its oceanographic and geomorphological characteristics. The Broad Sound was analyzed separately, as its high tidal range and distance from the major Whitsundays and Fitzroy Rivers make this area unrepresentative of the more intensely used and populated areas of the Whitsundays and Fitzroy NRM Regions. The boundaries were chosen to best match those of both the Whitsundays and Fitzroy areas.

The Cape York and Wet Tropics NRM regions were subdivided into three long-shore bands, with the ¿Cape York¿ band extending to 14.5 degrees latitude (Lizard Island), and a northern Wet Tropics region, split at Cape Grafton south of Cairns), and the southern Wet Tropics to best capture their differences in geomorphology, rainfall, agricultural use patterns, and population outbreak dynamics of crown-of-thorns starfish. The statistical methods to relate photic depth to river discharges are described in Fabricius KE, Logan M, Weeks S, Brodie J (2014) The effects of river run-off on water clarity across the central Great Barrier Reef. Marine Pollution Bulletin 84: 191-200, and in Murray Logan, Katharina Fabricius, Scarla Weeks, Ana Rodriguez, Stephen Lewis and Jon Brodie (2014) NERP Project 4.1: Tracking coastal turbidity over time and demonstrating the effects of river discharge events on regional turbidity in the GBR. NERP Progress Report: Southern and Northern NRM Regions. 63 pp Photic depth: The daily 1 km2 MODIS-Aqua remote sensing data were processed as described by Weeks et al. 2012, Fabricius et al. 2014. Masks were generated to excise optically shallow waters (reefs and very shallow coastal sections of the seabed), and offshore to >200 m bathymetry. As the full gridded daily data series is too large to reside in memory (153,177 grid points per day, over 11 years), it was spatially aggregated into the 35 zones. Data were aggregated to water years (1st October to 30th September) rather than calendar years. Data availability varied greatly between days and months due to cloud cover. To explore temporal differences in photic depth between wet and dry years, the analyses were also performed separately for dry (2002-2006) and wet (2007-2012) years.

Predicted daily tidal amplitudes as a proxy for tidal currents were obtained from the Australian Navy. For each zone, a single tidal location or a set of ¿representative¿ tidal locations was chosen, and the mean tidal range per day was calculated across these locations, to reduce computational exhaustion.

Hourly data on wave heights and wave frequencies were obtained from the Queensland State Government, Department of Environment and Heritage Protection (DEHP), from the 4 wave rider buoys available in the study region: Emu Point Buoy for the southern zones, Mackay Buoy for the Whitsunday zones, Townsville Buoy for the Burdekin zone, and Cairns Buoy for the Northern and Southern Wet Tropics. For the Cape York zones, wind data from the Bureau of Meteorology (http://www.bom.gov.au/oceanography/projects/abslmp/data/index.shtml) from Lockhart River were considered more representative than the wave data from the Cairns buoy.

The analyses was based on daily values and performed separately for each zone. In order to explore the long-term photic depth signals, the data were seasonally detrended and smoothed. Gradient boosted model (GBM) and generalized additive mixed effects models (GAMM) were fitted to remove the effects of tides and wind/waves. The residuals from these GAMM (which thus reflect the photic depth signal after the extraction of wave, tidal and bathymetry signals) were then decomposed to derive the intra-annual trends (i.e., seasonal based on 365.25 day cyclicity) and inter-annual trends in photic depth. Seasonal decomposition was chosen which applies a smoother (typically either a moving average or locally weighted regression smoother) through a time series to separate periodic fluctuations due to cyclical reoccurring influences and long-term trends. Following temporal decomposition, seasonal cycles were re-centered around mean GAMM fitted values, and transformed back into the original photic depth scale via exponentiation.

Limitations:

The analyses only investigated the effects of river runoff on water clarity. This does not indicate that other factors (e.g. coastal developments, dredging) do not additionally affect water clarity; such relationships would have to be investigated separately.

Format:

This dataset comprises 2 shape files and a csv file: - FabriciusAndLoganNerpDataCorrelations.* (142 kb) (dbf, shp and shx files), - FabriciusAndLoganNerpDataSummaries.* (142 kb) (dbf, shp and shx files) and - FabriciusAndLoganNerpSeasonalStatsDataRound.csv (3 kb).

Data Dictionary:

FabriciusAndLoganNerpDataCorrelations.shp:

The shapefile contains a set of polygon zones for 35 zones in the entire. The attributes table contains the strength of the correlation between daily river discharge and daily satellite photic depth, over 11 years.

The attributes are: - SP_ID: shape id - Correlatio: correlation value

FabriciusAndLoganNerpDataSummaries.shp:

The shapefile contains a set of polygon zones for 35 zones in the entire GBR. In each zone, we calculated the mean values of hourly or daily values, over 11 years.

The attributes are: - SP_ID: shape id - Photic_dep: photic depth (meters), means over 11 years of daily photic depth values, calculated based on an algorithm developed by Scarla Weeks (UQ) and NASA, (equivalent to Secchi depth) - Tidal_rang: tidal range (meters), means over 11 years of tidal range values (difference between highest and lowest sea-level within each day), calculated from tidal predictions of the Australian Navy. - Wave_heigh: wave height (meters),

Maximum MCIPI computed for each subregion when monthly and yearly aggregations were considered; dates are indicated between brackets. Decadal MCIPI (last column) and associated percentage of the total (all regions considered) were based on monthly aggregations over the 10-year period.

Data on the number of vessels, catch and number of days fished was collated by Fisheries Queensland.

Historical chart and dataset showing U.K. tourist spending by year from 1995 to 1998.